Comparison of methods for identifying periodically varying genes

Authors:
Vinaya Vijayan;Prachi Deshpande;Chetan Gadgil;Mugdha Gadgil
Affiliations:
Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India;Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India;Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India;Chemical Engineering and Process Development, National Chemical Laboratory, CSIR, Pune 411008, India
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2013

Citing 6
Cited 0

Identifying periodically expressed transcripts in microarray time series data

Bioinformatics
Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data

Bioinformatics
Comparison of computational methods for the identification of cell cycle-regulated genes

Bioinformatics
Identifying genes from up--down properties of microarray expression series

Bioinformatics
Detecting periodic patterns in unevenly spaced gene expression time series using Lomb--Scargle periodograms

Bioinformatics
Unbiased pattern detection in microarray data series

Bioinformatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Several methods have been reported for identifying periodically varying genes from gene expression datasets. We compare the performance of five existing methods and a combination of G-statistic and autocovariance (called GVAR) using simulated sine-function-based and cell-cycle-based datasets. Based on this analysis we recommend appropriate methods for different experimental situations (length of the time series, sampling interval and noise level). No single method performs the best under all tested conditions. None of the evaluated methods perform well at high noise levels for short time series data. At lower noise levels, GVAR performed the best.